Overview

Dataset statistics

Number of variables27
Number of observations20000
Missing cells0
Missing cells (%)0.0%
Duplicate rows96
Duplicate rows (%)0.5%
Total size in memory4.1 MiB
Average record size in memory216.0 B

Variable types

Numeric20
Categorical7

Alerts

Dataset has 96 (0.5%) duplicate rowsDuplicates
hour is highly overall correlated with uvIndexHigh correlation
day is highly overall correlated with windSpeed and 3 other fieldsHigh correlation
temperature is highly overall correlated with temperatureHigh and 1 other fieldsHigh correlation
precipIntensity is highly overall correlated with precipProbability and 5 other fieldsHigh correlation
precipProbability is highly overall correlated with precipIntensity and 6 other fieldsHigh correlation
humidity is highly overall correlated with precipIntensity and 5 other fieldsHigh correlation
windSpeed is highly overall correlated with day and 4 other fieldsHigh correlation
windGust is highly overall correlated with day and 4 other fieldsHigh correlation
visibility is highly overall correlated with precipIntensity and 2 other fieldsHigh correlation
temperatureHigh is highly overall correlated with temperature and 4 other fieldsHigh correlation
temperatureLow is highly overall correlated with temperatureHigh and 2 other fieldsHigh correlation
dewPoint is highly overall correlated with temperature and 8 other fieldsHigh correlation
pressure is highly overall correlated with day and 3 other fieldsHigh correlation
cloudCover is highly overall correlated with precipIntensity and 4 other fieldsHigh correlation
ozone is highly overall correlated with windSpeed and 3 other fieldsHigh correlation
precipIntensityMax is highly overall correlated with precipIntensity and 3 other fieldsHigh correlation
month is highly overall correlated with day and 7 other fieldsHigh correlation
cab_type is highly overall correlated with nameHigh correlation
name is highly overall correlated with cab_typeHigh correlation
short_summary is highly overall correlated with precipProbability and 1 other fieldsHigh correlation
uvIndex is highly overall correlated with hourHigh correlation
hour has 933 (4.7%) zerosZeros
precipIntensity has 15665 (78.3%) zerosZeros
precipProbability has 15665 (78.3%) zerosZeros
cloudCover has 1162 (5.8%) zerosZeros
precipIntensityMax has 6738 (33.7%) zerosZeros

Reproduction

Analysis started2023-03-19 14:42:55.810578
Analysis finished2023-03-19 14:44:01.226886
Duration1 minute and 5.42 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

hour
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.63105
Minimum0
Maximum23
Zeros933
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2023-03-19T20:14:01.314745image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median12
Q318
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.9783279
Coefficient of variation (CV)0.59997403
Kurtosis-1.1942012
Mean11.63105
Median Absolute Deviation (MAD)6
Skewness-0.045179801
Sum232621
Variance48.697061
MonotonicityNot monotonic
2023-03-19T20:14:01.437186image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
23 935
 
4.7%
0 933
 
4.7%
16 927
 
4.6%
15 881
 
4.4%
22 876
 
4.4%
10 865
 
4.3%
13 861
 
4.3%
14 858
 
4.3%
17 857
 
4.3%
12 850
 
4.2%
Other values (14) 11157
55.8%
ValueCountFrequency (%)
0 933
4.7%
1 829
4.1%
2 846
4.2%
3 789
3.9%
4 850
4.2%
5 719
3.6%
6 783
3.9%
7 711
3.6%
8 711
3.6%
9 836
4.2%
ValueCountFrequency (%)
23 935
4.7%
22 876
4.4%
21 796
4.0%
20 800
4.0%
19 808
4.0%
18 838
4.2%
17 857
4.3%
16 927
4.6%
15 881
4.4%
14 858
4.3%

day
Real number (ℝ)

Distinct17
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.03165
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2023-03-19T20:14:01.569090image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q113
median17
Q328
95-th percentile30
Maximum30
Range29
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.9591068
Coefficient of variation (CV)0.55231256
Kurtosis-1.1619105
Mean18.03165
Median Absolute Deviation (MAD)10
Skewness-0.41203252
Sum360633
Variance99.183807
MonotonicityNot monotonic
2023-03-19T20:14:01.683706image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
28 2263
11.3%
27 2256
11.3%
29 1810
 
9.0%
16 1293
 
6.5%
1 1289
 
6.4%
17 1277
 
6.4%
3 1253
 
6.3%
14 1252
 
6.3%
30 1243
 
6.2%
2 1227
 
6.1%
Other values (7) 4837
24.2%
ValueCountFrequency (%)
1 1289
6.4%
2 1227
6.1%
3 1253
6.3%
4 351
 
1.8%
9 56
 
0.3%
10 93
 
0.5%
13 1179
5.9%
14 1252
6.3%
15 1201
6.0%
16 1293
6.5%
ValueCountFrequency (%)
30 1243
6.2%
29 1810
9.0%
28 2263
11.3%
27 2256
11.3%
26 968
4.8%
18 989
4.9%
17 1277
6.4%
16 1293
6.5%
15 1201
6.0%
14 1252
6.3%

month
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
12
11460 
11
8540 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters40000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row12
2nd row11
3rd row11
4th row11
5th row11

Common Values

ValueCountFrequency (%)
12 11460
57.3%
11 8540
42.7%

Length

2023-03-19T20:14:01.809166image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-19T20:14:01.960440image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
12 11460
57.3%
11 8540
42.7%

Most occurring characters

ValueCountFrequency (%)
1 28540
71.4%
2 11460
28.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 28540
71.4%
2 11460
28.6%

Most occurring scripts

ValueCountFrequency (%)
Common 40000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 28540
71.4%
2 11460
28.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 28540
71.4%
2 11460
28.6%

source
Categorical

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
Theatre District
1815 
North Station
1782 
Financial District
1722 
Haymarket Square
1711 
Fenway
1711 
Other values (7)
11259 

Length

Max length23
Median length17
Mean length13.11805
Min length6

Characters and Unicode

Total characters262361
Distinct characters30
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHaymarket Square
2nd rowHaymarket Square
3rd rowHaymarket Square
4th rowHaymarket Square
5th rowHaymarket Square

Common Values

ValueCountFrequency (%)
Theatre District 1815
9.1%
North Station 1782
8.9%
Financial District 1722
8.6%
Haymarket Square 1711
8.6%
Fenway 1711
8.6%
West End 1687
8.4%
North End 1670
8.3%
Beacon Hill 1656
8.3%
Back Bay 1613
8.1%
Northeastern University 1547
7.7%
Other values (2) 3086
15.4%

Length

2023-03-19T20:14:02.094170image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
district 3537
 
9.2%
north 3452
 
9.0%
end 3357
 
8.8%
station 3325
 
8.7%
university 3090
 
8.1%
theatre 1815
 
4.7%
financial 1722
 
4.5%
square 1711
 
4.5%
fenway 1711
 
4.5%
haymarket 1711
 
4.5%
Other values (8) 12858
33.6%

Most occurring characters

ValueCountFrequency (%)
t 31659
12.1%
a 21857
 
8.3%
i 21679
 
8.3%
n 19673
 
7.5%
r 18410
 
7.0%
e 18290
 
7.0%
18289
 
7.0%
o 14609
 
5.6%
s 11404
 
4.3%
c 8528
 
3.3%
Other values (20) 77963
29.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 205783
78.4%
Uppercase Letter 38289
 
14.6%
Space Separator 18289
 
7.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 31659
15.4%
a 21857
10.6%
i 21679
10.5%
n 19673
9.6%
r 18410
8.9%
e 18290
8.9%
o 14609
7.1%
s 11404
 
5.5%
c 8528
 
4.1%
h 8357
 
4.1%
Other values (9) 31317
15.2%
Uppercase Letter
ValueCountFrequency (%)
S 6579
17.2%
B 6425
16.8%
N 4999
13.1%
D 3537
9.2%
F 3433
9.0%
H 3367
8.8%
E 3357
8.8%
U 3090
8.1%
T 1815
 
4.7%
W 1687
 
4.4%
Space Separator
ValueCountFrequency (%)
18289
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 244072
93.0%
Common 18289
 
7.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 31659
13.0%
a 21857
 
9.0%
i 21679
 
8.9%
n 19673
 
8.1%
r 18410
 
7.5%
e 18290
 
7.5%
o 14609
 
6.0%
s 11404
 
4.7%
c 8528
 
3.5%
h 8357
 
3.4%
Other values (19) 69606
28.5%
Common
ValueCountFrequency (%)
18289
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 262361
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 31659
12.1%
a 21857
 
8.3%
i 21679
 
8.3%
n 19673
 
7.5%
r 18410
 
7.0%
e 18290
 
7.0%
18289
 
7.0%
o 14609
 
5.6%
s 11404
 
4.3%
c 8528
 
3.3%
Other values (20) 77963
29.7%

destination
Categorical

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
North End
1859 
Haymarket Square
1743 
Northeastern University
1739 
Fenway
1719 
Beacon Hill
1713 
Other values (7)
11227 

Length

Max length23
Median length16
Mean length13.1381
Min length6

Characters and Unicode

Total characters262762
Distinct characters30
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorth Station
2nd rowNorth Station
3rd rowNorth Station
4th rowNorth Station
5th rowNorth Station

Common Values

ValueCountFrequency (%)
North End 1859
9.3%
Haymarket Square 1743
8.7%
Northeastern University 1739
8.7%
Fenway 1719
8.6%
Beacon Hill 1713
8.6%
North Station 1700
8.5%
Theatre District 1644
8.2%
Boston University 1624
8.1%
Back Bay 1593
8.0%
South Station 1591
8.0%
Other values (2) 3075
15.4%

Length

2023-03-19T20:14:02.240744image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
north 3559
 
9.3%
end 3439
 
9.0%
university 3363
 
8.8%
station 3291
 
8.6%
district 3139
 
8.2%
square 1743
 
4.6%
haymarket 1743
 
4.6%
northeastern 1739
 
4.5%
fenway 1719
 
4.5%
beacon 1713
 
4.5%
Other values (8) 12833
33.5%

Most occurring characters

ValueCountFrequency (%)
t 31442
12.0%
a 21511
 
8.2%
i 20998
 
8.0%
n 19878
 
7.6%
r 18669
 
7.1%
e 18627
 
7.1%
18281
 
7.0%
o 15141
 
5.8%
s 11445
 
4.4%
h 8533
 
3.2%
Other values (20) 78237
29.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 206200
78.5%
Uppercase Letter 38281
 
14.6%
Space Separator 18281
 
7.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 31442
15.2%
a 21511
10.4%
i 20998
10.2%
n 19878
9.6%
r 18669
9.1%
e 18627
9.0%
o 15141
7.3%
s 11445
 
5.6%
h 8533
 
4.1%
y 8418
 
4.1%
Other values (9) 31538
15.3%
Uppercase Letter
ValueCountFrequency (%)
S 6625
17.3%
B 6523
17.0%
N 5298
13.8%
H 3456
9.0%
E 3439
9.0%
U 3363
8.8%
F 3214
8.4%
D 3139
8.2%
T 1644
 
4.3%
W 1580
 
4.1%
Space Separator
ValueCountFrequency (%)
18281
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 244481
93.0%
Common 18281
 
7.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 31442
12.9%
a 21511
 
8.8%
i 20998
 
8.6%
n 19878
 
8.1%
r 18669
 
7.6%
e 18627
 
7.6%
o 15141
 
6.2%
s 11445
 
4.7%
h 8533
 
3.5%
y 8418
 
3.4%
Other values (19) 69819
28.6%
Common
ValueCountFrequency (%)
18281
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 262762
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 31442
12.0%
a 21511
 
8.2%
i 20998
 
8.0%
n 19878
 
7.6%
r 18669
 
7.1%
e 18627
 
7.1%
18281
 
7.0%
o 15141
 
5.8%
s 11445
 
4.4%
h 8533
 
3.2%
Other values (20) 78237
29.8%

cab_type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
Uber
10971 
Lyft
9029 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters80000
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLyft
2nd rowLyft
3rd rowLyft
4th rowLyft
5th rowLyft

Common Values

ValueCountFrequency (%)
Uber 10971
54.9%
Lyft 9029
45.1%

Length

2023-03-19T20:14:02.370757image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-19T20:14:02.523959image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
uber 10971
54.9%
lyft 9029
45.1%

Most occurring characters

ValueCountFrequency (%)
U 10971
13.7%
b 10971
13.7%
e 10971
13.7%
r 10971
13.7%
L 9029
11.3%
y 9029
11.3%
f 9029
11.3%
t 9029
11.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 60000
75.0%
Uppercase Letter 20000
 
25.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
b 10971
18.3%
e 10971
18.3%
r 10971
18.3%
y 9029
15.0%
f 9029
15.0%
t 9029
15.0%
Uppercase Letter
ValueCountFrequency (%)
U 10971
54.9%
L 9029
45.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 80000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 10971
13.7%
b 10971
13.7%
e 10971
13.7%
r 10971
13.7%
L 9029
11.3%
y 9029
11.3%
f 9029
11.3%
t 9029
11.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 10971
13.7%
b 10971
13.7%
e 10971
13.7%
r 10971
13.7%
L 9029
11.3%
y 9029
11.3%
f 9029
11.3%
t 9029
11.3%

name
Categorical

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
UberPool
1570 
UberX
1569 
Black SUV
1569 
Taxi
1569 
UberXL
1567 
Other values (8)
12156 

Length

Max length12
Median length7
Mean length6.22205
Min length3

Characters and Unicode

Total characters124441
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowShared
2nd rowLux
3rd rowLyft
4th rowLux Black XL
5th rowLyft XL

Common Values

ValueCountFrequency (%)
UberPool 1570
 
7.8%
UberX 1569
 
7.8%
Black SUV 1569
 
7.8%
Taxi 1569
 
7.8%
UberXL 1567
 
7.8%
Black 1566
 
7.8%
WAV 1561
 
7.8%
Lux 1516
 
7.6%
Lux Black XL 1513
 
7.6%
Lyft XL 1506
 
7.5%
Other values (3) 4494
22.5%

Length

2023-03-19T20:14:02.671507image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
black 6148
22.3%
lux 4529
16.4%
xl 3019
10.9%
lyft 2999
10.9%
uberpool 1570
 
5.7%
uberx 1569
 
5.7%
suv 1569
 
5.7%
taxi 1569
 
5.7%
uberxl 1567
 
5.7%
wav 1561
 
5.7%

Most occurring characters

ValueCountFrequency (%)
L 12114
 
9.7%
a 9218
 
7.4%
l 7718
 
6.2%
7601
 
6.1%
U 6275
 
5.0%
e 6207
 
5.0%
r 6207
 
5.0%
X 6155
 
4.9%
B 6148
 
4.9%
c 6148
 
4.9%
Other values (17) 50650
40.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 73687
59.2%
Uppercase Letter 43153
34.7%
Space Separator 7601
 
6.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 9218
12.5%
l 7718
10.5%
e 6207
8.4%
r 6207
8.4%
c 6148
8.3%
k 6148
8.3%
x 6098
8.3%
b 4706
 
6.4%
u 4529
 
6.1%
o 3140
 
4.3%
Other values (6) 13568
18.4%
Uppercase Letter
ValueCountFrequency (%)
L 12114
28.1%
U 6275
14.5%
X 6155
14.3%
B 6148
14.2%
V 3130
 
7.3%
S 3070
 
7.1%
P 1570
 
3.6%
T 1569
 
3.6%
W 1561
 
3.6%
A 1561
 
3.6%
Space Separator
ValueCountFrequency (%)
7601
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 116840
93.9%
Common 7601
 
6.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 12114
 
10.4%
a 9218
 
7.9%
l 7718
 
6.6%
U 6275
 
5.4%
e 6207
 
5.3%
r 6207
 
5.3%
X 6155
 
5.3%
B 6148
 
5.3%
c 6148
 
5.3%
k 6148
 
5.3%
Other values (16) 44502
38.1%
Common
ValueCountFrequency (%)
7601
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124441
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L 12114
 
9.7%
a 9218
 
7.4%
l 7718
 
6.2%
7601
 
6.1%
U 6275
 
5.0%
e 6207
 
5.0%
r 6207
 
5.0%
X 6155
 
4.9%
B 6148
 
4.9%
c 6148
 
4.9%
Other values (17) 50650
40.7%

price
Real number (ℝ)

Distinct107
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.430525
Minimum2.5
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2023-03-19T20:14:02.840789image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile6.5
Q19.5
median15.5
Q322.5
95-th percentile33
Maximum80
Range77.5
Interquartile range (IQR)13

Descriptive statistics

Standard deviation8.8617657
Coefficient of variation (CV)0.5393477
Kurtosis1.2322155
Mean16.430525
Median Absolute Deviation (MAD)6.5
Skewness1.0497155
Sum328610.49
Variance78.530891
MonotonicityNot monotonic
2023-03-19T20:14:03.013206image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.43052466 1569
 
7.8%
7 1512
 
7.6%
16.5 1310
 
6.6%
10.5 1145
 
5.7%
13.5 993
 
5.0%
9 935
 
4.7%
27.5 809
 
4.0%
26 778
 
3.9%
22.5 760
 
3.8%
19.5 738
 
3.7%
Other values (97) 9451
47.3%
ValueCountFrequency (%)
2.5 5
 
< 0.1%
3 176
 
0.9%
3.5 166
 
0.8%
4.5 6
 
< 0.1%
5 407
 
2.0%
5.5 71
 
0.4%
6 12
 
0.1%
6.5 163
 
0.8%
7 1512
7.6%
7.5 481
 
2.4%
ValueCountFrequency (%)
80 1
 
< 0.1%
75 1
 
< 0.1%
67.5 4
 
< 0.1%
65 4
 
< 0.1%
62 1
 
< 0.1%
58 1
 
< 0.1%
57.5 4
 
< 0.1%
56 2
 
< 0.1%
55 15
0.1%
54 1
 
< 0.1%

distance
Real number (ℝ)

Distinct397
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.148872
Minimum0.02
Maximum7.46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2023-03-19T20:14:03.201611image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile0.57
Q11.25
median2.08
Q32.86
95-th percentile4.43
Maximum7.46
Range7.44
Interquartile range (IQR)1.61

Descriptive statistics

Standard deviation1.124711
Coefficient of variation (CV)0.52339598
Kurtosis1.4338639
Mean2.148872
Median Absolute Deviation (MAD)0.8
Skewness0.88726409
Sum42977.44
Variance1.2649747
MonotonicityNot monotonic
2023-03-19T20:14:03.374875image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.05 307
 
1.5%
2.66 292
 
1.5%
1.41 289
 
1.4%
1.5 288
 
1.4%
1.35 275
 
1.4%
1.16 264
 
1.3%
1.08 254
 
1.3%
2.32 251
 
1.3%
1.34 235
 
1.2%
1.25 225
 
1.1%
Other values (387) 17320
86.6%
ValueCountFrequency (%)
0.02 7
 
< 0.1%
0.03 1
 
< 0.1%
0.12 7
 
< 0.1%
0.3 7
 
< 0.1%
0.39 129
0.6%
0.4 6
 
< 0.1%
0.42 6
 
< 0.1%
0.43 41
 
0.2%
0.44 33
 
0.2%
0.45 36
 
0.2%
ValueCountFrequency (%)
7.46 51
0.3%
7.34 7
 
< 0.1%
7.2 13
 
0.1%
6.26 28
 
0.1%
5.7 14
 
0.1%
5.56 76
0.4%
5.46 7
 
< 0.1%
5.44 6
 
< 0.1%
5.42 6
 
< 0.1%
5.41 10
 
0.1%

surge_multiplier
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0151
Minimum1
Maximum2.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2023-03-19T20:14:03.547449image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum2.5
Range1.5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.094920706
Coefficient of variation (CV)0.093508725
Kurtosis71.868367
Mean1.0151
Median Absolute Deviation (MAD)0
Skewness7.8857464
Sum20302
Variance0.0090099405
MonotonicityNot monotonic
2023-03-19T20:14:03.657283image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 19334
96.7%
1.25 361
 
1.8%
1.5 145
 
0.7%
1.75 95
 
0.5%
2 59
 
0.3%
2.5 6
 
< 0.1%
ValueCountFrequency (%)
1 19334
96.7%
1.25 361
 
1.8%
1.5 145
 
0.7%
1.75 95
 
0.5%
2 59
 
0.3%
2.5 6
 
< 0.1%
ValueCountFrequency (%)
2.5 6
 
< 0.1%
2 59
 
0.3%
1.75 95
 
0.5%
1.5 145
 
0.7%
1.25 361
 
1.8%
1 19334
96.7%

temperature
Real number (ℝ)

Distinct308
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.523206
Minimum18.91
Maximum57.22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2023-03-19T20:14:03.830245image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum18.91
5-th percentile27.41
Q136.5
median40.49
Q343.57
95-th percentile49.34
Maximum57.22
Range38.31
Interquartile range (IQR)7.07

Descriptive statistics

Standard deviation6.7285255
Coefficient of variation (CV)0.1702424
Kurtosis0.81048026
Mean39.523206
Median Absolute Deviation (MAD)3.42
Skewness-0.64050184
Sum790464.12
Variance45.273055
MonotonicityNot monotonic
2023-03-19T20:14:04.002945image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.92 211
 
1.1%
39.41 200
 
1.0%
41.24 176
 
0.9%
41.47 175
 
0.9%
38.42 175
 
0.9%
40.43 172
 
0.9%
39.35 165
 
0.8%
41.16 160
 
0.8%
40.13 148
 
0.7%
36.53 146
 
0.7%
Other values (298) 18272
91.4%
ValueCountFrequency (%)
18.91 53
0.3%
18.97 51
0.3%
19.28 52
0.3%
20.01 49
0.2%
20.07 65
0.3%
20.23 62
0.3%
20.38 76
0.4%
20.42 49
0.2%
22.18 59
0.3%
23.19 56
0.3%
ValueCountFrequency (%)
57.22 51
0.3%
54.62 52
0.3%
54.59 50
0.2%
54.38 51
0.3%
53.51 58
0.3%
53.34 52
0.3%
53.1 43
0.2%
52.9 53
0.3%
52.68 54
0.3%
52.45 68
0.3%

short_summary
Categorical

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
Overcast
6273 
Mostly Cloudy
4334 
Partly Cloudy
3597 
Clear
2521 
Light Rain
1553 
Other values (4)
1722 

Length

Max length18
Median length15
Mean length11.7962
Min length6

Characters and Unicode

Total characters235924
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row Mostly Cloudy
2nd row Rain
3rd row Clear
4th row Clear
5th row Partly Cloudy

Common Values

ValueCountFrequency (%)
Overcast 6273
31.4%
Mostly Cloudy 4334
21.7%
Partly Cloudy 3597
18.0%
Clear 2521
12.6%
Light Rain 1553
 
7.8%
Rain 695
 
3.5%
Possible Drizzle 563
 
2.8%
Foggy 267
 
1.3%
Drizzle 197
 
1.0%

Length

2023-03-19T20:14:04.175688image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-19T20:14:04.348261image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
cloudy 7931
26.4%
overcast 6273
20.9%
mostly 4334
14.4%
partly 3597
12.0%
clear 2521
 
8.4%
rain 2248
 
7.5%
light 1553
 
5.2%
drizzle 760
 
2.5%
possible 563
 
1.9%
foggy 267
 
0.9%

Most occurring characters

ValueCountFrequency (%)
50047
21.2%
l 19706
 
8.4%
y 16129
 
6.8%
t 15757
 
6.7%
a 14639
 
6.2%
r 13151
 
5.6%
o 13095
 
5.6%
s 11733
 
5.0%
C 10452
 
4.4%
e 10117
 
4.3%
Other values (17) 61098
25.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 155830
66.1%
Space Separator 50047
 
21.2%
Uppercase Letter 30047
 
12.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 19706
12.6%
y 16129
10.4%
t 15757
10.1%
a 14639
9.4%
r 13151
8.4%
o 13095
8.4%
s 11733
7.5%
e 10117
 
6.5%
u 7931
 
5.1%
d 7931
 
5.1%
Other values (8) 25641
16.5%
Uppercase Letter
ValueCountFrequency (%)
C 10452
34.8%
O 6273
20.9%
M 4334
14.4%
P 4160
 
13.8%
R 2248
 
7.5%
L 1553
 
5.2%
D 760
 
2.5%
F 267
 
0.9%
Space Separator
ValueCountFrequency (%)
50047
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 185877
78.8%
Common 50047
 
21.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 19706
 
10.6%
y 16129
 
8.7%
t 15757
 
8.5%
a 14639
 
7.9%
r 13151
 
7.1%
o 13095
 
7.0%
s 11733
 
6.3%
C 10452
 
5.6%
e 10117
 
5.4%
u 7931
 
4.3%
Other values (16) 53167
28.6%
Common
ValueCountFrequency (%)
50047
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 235924
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
50047
21.2%
l 19706
 
8.4%
y 16129
 
6.8%
t 15757
 
6.7%
a 14639
 
6.2%
r 13151
 
5.6%
o 13095
 
5.6%
s 11733
 
5.0%
C 10452
 
4.4%
e 10117
 
4.3%
Other values (17) 61098
25.9%

precipIntensity
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct63
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00892266
Minimum0
Maximum0.1447
Zeros15665
Zeros (%)78.3%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2023-03-19T20:14:04.567952image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.0786
Maximum0.1447
Range0.1447
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.026940218
Coefficient of variation (CV)3.0193034
Kurtosis10.196862
Mean0.00892266
Median Absolute Deviation (MAD)0
Skewness3.3091419
Sum178.4532
Variance0.00072577534
MonotonicityNot monotonic
2023-03-19T20:14:04.741296image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15665
78.3%
0.002 161
 
0.8%
0.0005 149
 
0.7%
0.1058 114
 
0.6%
0.0013 106
 
0.5%
0.0021 105
 
0.5%
0.007 105
 
0.5%
0.1264 105
 
0.5%
0.0674 103
 
0.5%
0.1299 102
 
0.5%
Other values (53) 3285
 
16.4%
ValueCountFrequency (%)
0 15665
78.3%
0.0002 46
 
0.2%
0.0003 89
 
0.4%
0.0005 149
 
0.7%
0.0006 46
 
0.2%
0.0009 52
 
0.3%
0.001 50
 
0.2%
0.0012 61
 
0.3%
0.0013 106
 
0.5%
0.0015 90
 
0.4%
ValueCountFrequency (%)
0.1447 70
0.4%
0.1299 102
0.5%
0.1289 49
0.2%
0.1267 101
0.5%
0.1264 105
0.5%
0.1088 98
0.5%
0.1058 114
0.6%
0.1044 56
0.3%
0.0923 55
0.3%
0.092 60
0.3%

precipProbability
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.145447
Minimum0
Maximum1
Zeros15665
Zeros (%)78.3%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2023-03-19T20:14:04.913989image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.32778975
Coefficient of variation (CV)2.2536715
Kurtosis2.3733927
Mean0.145447
Median Absolute Deviation (MAD)0
Skewness2.0351767
Sum2908.94
Variance0.10744612
MonotonicityNot monotonic
2023-03-19T20:14:05.070744image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 15665
78.3%
1 2048
 
10.2%
0.29 163
 
0.8%
0.03 150
 
0.8%
0.94 144
 
0.7%
0.32 144
 
0.7%
0.14 126
 
0.6%
0.59 113
 
0.6%
0.11 109
 
0.5%
0.61 105
 
0.5%
Other values (19) 1233
 
6.2%
ValueCountFrequency (%)
0 15665
78.3%
0.02 41
 
0.2%
0.03 150
 
0.8%
0.07 43
 
0.2%
0.09 100
 
0.5%
0.1 98
 
0.5%
0.11 109
 
0.5%
0.12 93
 
0.5%
0.14 126
 
0.6%
0.15 89
 
0.4%
ValueCountFrequency (%)
1 2048
10.2%
0.99 56
 
0.3%
0.94 144
 
0.7%
0.86 47
 
0.2%
0.85 48
 
0.2%
0.74 102
 
0.5%
0.66 60
 
0.3%
0.61 105
 
0.5%
0.59 113
 
0.6%
0.57 49
 
0.2%

humidity
Real number (ℝ)

Distinct51
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.740443
Minimum0.38
Maximum0.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2023-03-19T20:14:05.227862image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0.38
5-th percentile0.52
Q10.64
median0.71
Q30.88
95-th percentile0.94
Maximum0.96
Range0.58
Interquartile range (IQR)0.24

Descriptive statistics

Standard deviation0.13841525
Coefficient of variation (CV)0.18693573
Kurtosis-1.0379848
Mean0.740443
Median Absolute Deviation (MAD)0.11
Skewness-0.059004532
Sum14808.86
Variance0.019158782
MonotonicityNot monotonic
2023-03-19T20:14:05.385734image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.69 1138
 
5.7%
0.91 1063
 
5.3%
0.7 989
 
4.9%
0.71 866
 
4.3%
0.92 833
 
4.2%
0.64 747
 
3.7%
0.93 730
 
3.6%
0.94 713
 
3.6%
0.6 627
 
3.1%
0.65 583
 
2.9%
Other values (41) 11711
58.6%
ValueCountFrequency (%)
0.38 17
 
0.1%
0.4 59
 
0.3%
0.41 39
 
0.2%
0.44 100
 
0.5%
0.46 147
0.7%
0.5 240
1.2%
0.51 344
1.7%
0.52 281
1.4%
0.53 99
 
0.5%
0.54 336
1.7%
ValueCountFrequency (%)
0.96 302
 
1.5%
0.95 383
 
1.9%
0.94 713
3.6%
0.93 730
3.6%
0.92 833
4.2%
0.91 1063
5.3%
0.9 362
 
1.8%
0.89 491
2.5%
0.88 524
2.6%
0.87 161
 
0.8%

windSpeed
Real number (ℝ)

Distinct291
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2286145
Minimum0.45
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2023-03-19T20:14:05.557447image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0.45
5-th percentile1.93
Q13.44
median5.92
Q38.41
95-th percentile11.98
Maximum15
Range14.55
Interquartile range (IQR)4.97

Descriptive statistics

Standard deviation3.14091
Coefficient of variation (CV)0.50427105
Kurtosis-0.5993049
Mean6.2286145
Median Absolute Deviation (MAD)2.49
Skewness0.4176952
Sum124572.29
Variance9.8653154
MonotonicityNot monotonic
2023-03-19T20:14:05.745765image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.33 274
 
1.4%
8.11 250
 
1.2%
4.54 232
 
1.2%
8.41 217
 
1.1%
8.28 209
 
1.0%
8.52 179
 
0.9%
8.39 176
 
0.9%
9.08 172
 
0.9%
9.63 165
 
0.8%
3.02 164
 
0.8%
Other values (281) 17962
89.8%
ValueCountFrequency (%)
0.45 14
 
0.1%
0.51 4
 
< 0.1%
0.53 28
0.1%
1.03 44
0.2%
1.05 59
0.3%
1.1 42
0.2%
1.25 48
0.2%
1.35 59
0.3%
1.44 48
0.2%
1.63 51
0.3%
ValueCountFrequency (%)
15 61
0.3%
14.95 44
0.2%
14.36 48
0.2%
14.3 51
0.3%
14.15 50
0.2%
14.11 43
0.2%
13.75 62
0.3%
13.14 39
0.2%
12.84 56
0.3%
12.82 48
0.2%

windGust
Real number (ℝ)

Distinct286
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5150405
Minimum0.8
Maximum27.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2023-03-19T20:14:05.925074image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0.8
5-th percentile2.53
Q14.06
median7.56
Q311.98
95-th percentile17.68
Maximum27.25
Range26.45
Interquartile range (IQR)7.92

Descriptive statistics

Standard deviation5.2609536
Coefficient of variation (CV)0.61784247
Kurtosis1.0725721
Mean8.5150405
Median Absolute Deviation (MAD)3.8
Skewness1.0763007
Sum170300.81
Variance27.677633
MonotonicityNot monotonic
2023-03-19T20:14:06.107052image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.47 247
 
1.2%
12.38 226
 
1.1%
12.21 200
 
1.0%
11.54 175
 
0.9%
12.72 172
 
0.9%
14.39 165
 
0.8%
4.16 163
 
0.8%
12.43 161
 
0.8%
12.76 160
 
0.8%
7.78 158
 
0.8%
Other values (276) 18173
90.9%
ValueCountFrequency (%)
0.8 4
 
< 0.1%
0.87 14
 
0.1%
0.88 28
 
0.1%
1.05 59
0.3%
1.44 48
0.2%
1.81 45
0.2%
1.83 49
0.2%
2.01 96
0.5%
2.03 10
 
0.1%
2.09 48
0.2%
ValueCountFrequency (%)
27.25 51
0.3%
26.67 48
0.2%
26.56 50
0.2%
25.75 62
0.3%
25.17 48
0.2%
24.98 61
0.3%
24.43 43
0.2%
23.96 44
0.2%
23.67 57
0.3%
22.48 49
0.2%

visibility
Real number (ℝ)

Distinct227
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4748685
Minimum0.717
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2023-03-19T20:14:06.311158image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0.717
5-th percentile2.683
Q18.432
median9.882
Q39.997
95-th percentile10
Maximum10
Range9.283
Interquartile range (IQR)1.565

Descriptive statistics

Standard deviation2.5954957
Coefficient of variation (CV)0.30625793
Kurtosis0.64846071
Mean8.4748685
Median Absolute Deviation (MAD)0.118
Skewness-1.509204
Sum169497.37
Variance6.7365978
MonotonicityNot monotonic
2023-03-19T20:14:06.483610image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 4957
 
24.8%
9.933 247
 
1.2%
9.972 224
 
1.1%
9.996 221
 
1.1%
9.961 204
 
1.0%
9.908 196
 
1.0%
9.974 185
 
0.9%
9.915 175
 
0.9%
9.725 167
 
0.8%
9.981 161
 
0.8%
Other values (217) 13263
66.3%
ValueCountFrequency (%)
0.717 59
0.3%
0.965 45
0.2%
1.348 47
0.2%
1.413 48
0.2%
1.46 56
0.3%
1.588 52
0.3%
1.685 4
 
< 0.1%
1.824 55
0.3%
2.03 47
0.2%
2.069 40
0.2%
ValueCountFrequency (%)
10 4957
24.8%
9.997 53
 
0.3%
9.996 221
 
1.1%
9.995 50
 
0.2%
9.994 42
 
0.2%
9.991 84
 
0.4%
9.99 61
 
0.3%
9.984 112
 
0.6%
9.981 161
 
0.8%
9.98 57
 
0.3%

temperatureHigh
Real number (ℝ)

Distinct129
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.950331
Minimum32.68
Maximum57.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2023-03-19T20:14:06.656056image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum32.68
5-th percentile33.81
Q142.57
median44.66
Q346.91
95-th percentile57.04
Maximum57.87
Range25.19
Interquartile range (IQR)4.34

Descriptive statistics

Standard deviation5.9379623
Coefficient of variation (CV)0.13210052
Kurtosis0.21338126
Mean44.950331
Median Absolute Deviation (MAD)2.17
Skewness0.085838956
Sum899006.63
Variance35.259396
MonotonicityNot monotonic
2023-03-19T20:14:06.844640image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42.6 857
 
4.3%
42.61 812
 
4.1%
42.57 458
 
2.3%
46.49 443
 
2.2%
44.66 427
 
2.1%
42.18 405
 
2.0%
54.29 397
 
2.0%
41.44 344
 
1.7%
54.47 335
 
1.7%
47.11 329
 
1.6%
Other values (119) 15193
76.0%
ValueCountFrequency (%)
32.68 56
 
0.3%
32.75 107
0.5%
32.8 141
0.7%
32.81 162
0.8%
32.84 154
0.8%
32.91 96
0.5%
32.97 17
 
0.1%
33.51 102
0.5%
33.62 59
 
0.3%
33.78 101
0.5%
ValueCountFrequency (%)
57.87 300
1.5%
57.52 161
0.8%
57.42 171
0.9%
57.27 221
1.1%
57.08 94
 
0.5%
57.04 103
 
0.5%
57.02 48
 
0.2%
56.89 143
0.7%
54.48 45
 
0.2%
54.47 335
1.7%

temperatureLow
Real number (ℝ)

Distinct133
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.17629
Minimum17.85
Maximum46.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2023-03-19T20:14:07.033239image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum17.85
5-th percentile21.91
Q130.17
median34.19
Q338.48
95-th percentile44.88
Maximum46.6
Range28.75
Interquartile range (IQR)8.31

Descriptive statistics

Standard deviation6.3590855
Coefficient of variation (CV)0.18606717
Kurtosis-0.40832493
Mean34.17629
Median Absolute Deviation (MAD)4.29
Skewness-0.34946968
Sum683525.8
Variance40.437969
MonotonicityNot monotonic
2023-03-19T20:14:07.190504image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.6 615
 
3.1%
41.9 443
 
2.2%
37.44 435
 
2.2%
37.33 422
 
2.1%
36.71 405
 
2.0%
27.05 389
 
1.9%
33.6 356
 
1.8%
33.85 329
 
1.6%
37.59 327
 
1.6%
40.76 324
 
1.6%
Other values (123) 15955
79.8%
ValueCountFrequency (%)
17.85 44
 
0.2%
18.31 56
 
0.3%
19.36 48
 
0.2%
19.63 48
 
0.2%
20.66 107
0.5%
20.68 56
 
0.3%
20.72 154
0.8%
20.88 141
0.7%
20.9 96
0.5%
20.91 62
0.3%
ValueCountFrequency (%)
46.6 272
1.4%
45.04 192
1.0%
44.99 116
0.6%
44.97 204
1.0%
44.96 151
0.8%
44.88 163
0.8%
44.79 96
 
0.5%
44.71 46
 
0.2%
42.2 103
 
0.5%
42.17 146
0.7%

dewPoint
Real number (ℝ)

Distinct313
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.57719
Minimum4.39
Maximum50.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2023-03-19T20:14:07.362560image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum4.39
5-th percentile10.79
Q127.49
median30.3
Q338.12
95-th percentile45.86
Maximum50.67
Range46.28
Interquartile range (IQR)10.63

Descriptive statistics

Standard deviation9.1538953
Coefficient of variation (CV)0.28988949
Kurtosis0.43746356
Mean31.57719
Median Absolute Deviation (MAD)4.06
Skewness-0.50035065
Sum631543.79
Variance83.793799
MonotonicityNot monotonic
2023-03-19T20:14:07.535402image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.39 250
 
1.2%
30.16 248
 
1.2%
28.36 189
 
0.9%
28.42 176
 
0.9%
30.3 175
 
0.9%
29.27 172
 
0.9%
29.86 165
 
0.8%
29.42 161
 
0.8%
28.91 160
 
0.8%
28.14 146
 
0.7%
Other values (303) 18158
90.8%
ValueCountFrequency (%)
4.39 44
 
0.2%
5.3 56
0.3%
6.46 52
0.3%
6.89 65
0.3%
7.06 53
0.3%
8.55 44
 
0.2%
8.68 48
0.2%
8.84 62
0.3%
9.15 110
0.5%
9.22 61
0.3%
ValueCountFrequency (%)
50.67 58
0.3%
49.27 51
0.3%
48.63 68
0.3%
48.54 47
0.2%
48.02 54
0.3%
47.81 53
0.3%
47.28 46
0.2%
47.14 101
0.5%
47.06 48
0.2%
46.76 49
0.2%

pressure
Real number (ℝ)

Distinct316
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1009.8596
Minimum988.09
Maximum1035.55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2023-03-19T20:14:07.692189image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum988.09
5-th percentile991
Q1999.71
median1008.97
Q31021.74
95-th percentile1033.94
Maximum1035.55
Range47.46
Interquartile range (IQR)22.03

Descriptive statistics

Standard deviation13.54757
Coefficient of variation (CV)0.0134153
Kurtosis-1.0914581
Mean1009.8596
Median Absolute Deviation (MAD)11.6
Skewness0.17708378
Sum20197192
Variance183.53665
MonotonicityNot monotonic
2023-03-19T20:14:07.864486image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
996.21 200
 
1.0%
991.46 187
 
0.9%
996.92 175
 
0.9%
994.99 172
 
0.9%
995.3 165
 
0.8%
997.37 161
 
0.8%
991.41 160
 
0.8%
991.36 146
 
0.7%
991.07 141
 
0.7%
991.12 139
 
0.7%
Other values (306) 18354
91.8%
ValueCountFrequency (%)
988.09 105
0.5%
988.29 93
0.5%
988.3 90
0.4%
988.85 80
0.4%
989.46 89
0.4%
989.5 95
0.5%
989.98 90
0.4%
990.16 97
0.5%
990.2 84
0.4%
990.25 93
0.5%
ValueCountFrequency (%)
1035.55 42
 
0.2%
1035.42 44
 
0.2%
1035.3 45
 
0.2%
1035.14 46
0.2%
1035.06 55
0.3%
1035 113
0.6%
1034.9 48
0.2%
1034.88 57
0.3%
1034.76 51
0.3%
1034.42 49
0.2%

windBearing
Real number (ℝ)

Distinct195
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean220.25635
Minimum2
Maximum356
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2023-03-19T20:14:08.037518image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile44
Q1124
median258
Q3303
95-th percentile339
Maximum356
Range354
Interquartile range (IQR)179

Descriptive statistics

Standard deviation99.388154
Coefficient of variation (CV)0.45123854
Kurtosis-1.0259112
Mean220.25635
Median Absolute Deviation (MAD)55
Skewness-0.63273177
Sum4405127
Variance9878.0051
MonotonicityNot monotonic
2023-03-19T20:14:08.225774image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
295 547
 
2.7%
303 530
 
2.6%
297 511
 
2.6%
313 401
 
2.0%
294 351
 
1.8%
305 319
 
1.6%
252 287
 
1.4%
296 273
 
1.4%
310 269
 
1.3%
314 262
 
1.3%
Other values (185) 16250
81.2%
ValueCountFrequency (%)
2 121
0.6%
13 95
0.5%
16 59
0.3%
18 66
0.3%
19 55
0.3%
21 48
 
0.2%
23 48
 
0.2%
34 51
0.3%
35 46
 
0.2%
36 55
0.3%
ValueCountFrequency (%)
356 59
 
0.3%
353 53
 
0.3%
352 45
 
0.2%
350 62
0.3%
349 115
0.6%
346 57
 
0.3%
345 52
 
0.3%
344 107
0.5%
342 152
0.8%
341 43
 
0.2%

cloudCover
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct83
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.687012
Minimum0
Maximum1
Zeros1162
Zeros (%)5.8%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2023-03-19T20:14:08.399201image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.37
median0.82
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.63

Descriptive statistics

Standard deviation0.3590791
Coefficient of variation (CV)0.52266787
Kurtosis-0.98667438
Mean0.687012
Median Absolute Deviation (MAD)0.18
Skewness-0.74225793
Sum13740.24
Variance0.1289378
MonotonicityNot monotonic
2023-03-19T20:14:08.602748image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 7791
39.0%
0 1162
 
5.8%
0.81 645
 
3.2%
0.77 464
 
2.3%
0.99 395
 
2.0%
0.25 359
 
1.8%
0.54 302
 
1.5%
0.37 301
 
1.5%
0.44 293
 
1.5%
0.12 280
 
1.4%
Other values (73) 8008
40.0%
ValueCountFrequency (%)
0 1162
5.8%
0.01 130
 
0.7%
0.02 211
 
1.1%
0.03 215
 
1.1%
0.04 152
 
0.8%
0.05 58
 
0.3%
0.06 113
 
0.6%
0.08 115
 
0.6%
0.09 42
 
0.2%
0.1 43
 
0.2%
ValueCountFrequency (%)
1 7791
39.0%
0.99 395
 
2.0%
0.98 143
 
0.7%
0.97 74
 
0.4%
0.96 114
 
0.6%
0.95 162
 
0.8%
0.94 45
 
0.2%
0.93 196
 
1.0%
0.92 226
 
1.1%
0.91 153
 
0.8%

uvIndex
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
0
15456 
1
4176 
2
 
368

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15456
77.3%
1 4176
 
20.9%
2 368
 
1.8%

Length

2023-03-19T20:14:08.743756image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-19T20:14:08.885707image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
0 15456
77.3%
1 4176
 
20.9%
2 368
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 15456
77.3%
1 4176
 
20.9%
2 368
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15456
77.3%
1 4176
 
20.9%
2 368
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common 20000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15456
77.3%
1 4176
 
20.9%
2 368
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15456
77.3%
1 4176
 
20.9%
2 368
 
1.8%

ozone
Real number (ℝ)

Distinct274
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean314.01858
Minimum269.4
Maximum378.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2023-03-19T20:14:09.042566image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum269.4
5-th percentile273.9
Q1291.1
median308.4
Q3333.5
95-th percentile362.8
Maximum378.9
Range109.5
Interquartile range (IQR)42.4

Descriptive statistics

Standard deviation27.777469
Coefficient of variation (CV)0.088458044
Kurtosis-0.90815607
Mean314.01858
Median Absolute Deviation (MAD)19.7
Skewness0.38201965
Sum6280371.6
Variance771.5878
MonotonicityNot monotonic
2023-03-19T20:14:09.215288image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
355 301
 
1.5%
290.3 236
 
1.2%
291.1 210
 
1.1%
352.8 200
 
1.0%
326.9 183
 
0.9%
296 176
 
0.9%
351.9 176
 
0.9%
349.9 175
 
0.9%
354.8 172
 
0.9%
325.3 167
 
0.8%
Other values (264) 18004
90.0%
ValueCountFrequency (%)
269.4 41
 
0.2%
269.6 49
0.2%
269.8 48
0.2%
269.9 46
0.2%
270.1 60
0.3%
270.5 58
0.3%
271.2 109
0.5%
271.5 55
0.3%
271.7 105
0.5%
271.9 50
0.2%
ValueCountFrequency (%)
378.9 39
0.2%
378.7 56
0.3%
377.1 59
0.3%
376.9 43
0.2%
376.8 48
0.2%
376.6 39
0.2%
375.9 48
0.2%
373.7 62
0.3%
372.6 17
 
0.1%
371.4 44
0.2%

precipIntensityMax
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct65
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03760078
Minimum0
Maximum0.1459
Zeros6738
Zeros (%)33.7%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2023-03-19T20:14:09.403764image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.0004
Q30.0954
95-th percentile0.14252
Maximum0.1459
Range0.1459
Interquartile range (IQR)0.0954

Descriptive statistics

Standard deviation0.055430544
Coefficient of variation (CV)1.474186
Kurtosis-0.89961472
Mean0.03760078
Median Absolute Deviation (MAD)0.0004
Skewness0.9807459
Sum752.0156
Variance0.0030725452
MonotonicityNot monotonic
2023-03-19T20:14:09.576321image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6738
33.7%
0.0001 1737
 
8.7%
0.0004 1256
 
6.3%
0.0003 626
 
3.1%
0.143 514
 
2.6%
0.0028 466
 
2.3%
0.1225 443
 
2.2%
0.0074 335
 
1.7%
0.1246 333
 
1.7%
0.13 322
 
1.6%
Other values (55) 7230
36.1%
ValueCountFrequency (%)
0 6738
33.7%
0.0001 1737
 
8.7%
0.0003 626
 
3.1%
0.0004 1256
 
6.3%
0.0005 262
 
1.3%
0.0007 300
 
1.5%
0.0028 466
 
2.3%
0.0029 113
 
0.6%
0.0056 154
 
0.8%
0.0074 335
 
1.7%
ValueCountFrequency (%)
0.1459 14
 
0.1%
0.1438 104
 
0.5%
0.1433 263
1.3%
0.143 514
2.6%
0.1429 105
 
0.5%
0.1425 175
 
0.9%
0.1422 191
 
1.0%
0.142 93
 
0.5%
0.1419 84
 
0.4%
0.1396 4
 
< 0.1%

Interactions

2023-03-19T20:13:56.826484image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:12:58.749902image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:01.593379image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:04.693283image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:07.776314image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:10.976570image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:14.093719image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:17.187163image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:20.529753image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:23.476494image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:26.442974image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:29.676519image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:32.626361image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:35.475891image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:38.410060image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:41.413371image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2023-03-19T20:13:49.177502image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:52.451184image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:55.392980image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:58.922726image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:00.328654image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:03.226021image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:06.415015image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:09.490797image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:12.692715image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:15.815551image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:19.142955image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:22.196721image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:25.113827image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:28.159236image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:31.297975image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:34.236365image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:37.109384image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:40.108229image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:43.354125image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:46.287142image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:49.327274image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:52.576468image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:55.566156image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:59.062404image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:00.458863image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:03.376500image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:06.542548image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:09.625965image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:12.842395image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:15.967929image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:19.294337image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:22.327162image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:25.242548image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:28.303753image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:31.442202image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:34.360147image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:37.258443image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:40.243065image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:43.481598image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:46.410261image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:49.476934image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:52.730033image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:55.705233image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:59.190779image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:00.608112image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:03.525618image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:06.710420image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:09.772144image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:12.982891image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:16.110014image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:19.466170image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:22.472085image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:25.409289image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:28.441944image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:31.589029image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:34.491998image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:37.376664image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:40.385592image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:43.627188image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:46.542942image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:49.624568image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:52.874603image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:55.826971image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:59.326699image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:00.743021image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:03.660259image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:06.866413image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:09.909806image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:13.158870image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:16.256462image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:19.609033image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:22.608883image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:25.543043image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:28.591886image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:31.725605image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:34.626012image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:37.526654image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:40.509355image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:43.760393image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:46.676157image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:49.778202image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:53.010514image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:55.984383image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:59.477253image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:00.873599image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:03.792084image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:07.008911image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:10.059207image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:13.309741image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:16.413650image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:19.772638image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:22.742588image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:25.693580image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:28.726501image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:31.882898image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:34.770291image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:37.661196image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2023-03-19T20:13:49.920537image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2023-03-19T20:13:59.625693image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:01.008704image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:04.106289image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:07.159779image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:10.201442image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:13.456291image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:16.539120image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:19.924120image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:22.876122image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:25.840797image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:28.876659image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:32.016261image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:34.893957image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:37.810826image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:40.793465image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:44.024292image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:46.959638image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:50.067016image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:53.307461image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:56.261534image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:59.776710image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:01.159702image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:04.261585image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:07.322715image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:10.531134image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:13.609627image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:16.696234image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:20.076662image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:23.042509image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:26.004334image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:29.280930image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:32.175794image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:35.041940image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:37.976132image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:40.942553image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:44.176957image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:47.109532image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:50.228461image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:53.460762image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:56.410469image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:59.926936image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:01.305124image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:04.405771image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:07.469548image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:10.675523image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:13.760492image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:16.868872image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:20.227976image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:23.189683image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:26.142612image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:29.441627image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:32.326439image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:35.193043image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:38.110663image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:41.097797image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:44.325874image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:47.243575image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:50.377582image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:53.616011image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:56.558194image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:14:00.093335image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:01.425371image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:04.543325image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:07.609165image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:10.822945image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:13.910374image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:17.010704image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:20.359234image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:23.328639image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:26.290872image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:29.542487image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:32.474169image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:35.326809image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:38.258949image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:41.241882image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:44.460198image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:47.398814image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:50.521714image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:53.757860image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-03-19T20:13:56.677300image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Correlations

2023-03-19T20:14:09.764334image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
hourdaypricedistancesurge_multipliertemperatureprecipIntensityprecipProbabilityhumiditywindSpeedwindGustvisibilitytemperatureHightemperatureLowdewPointpressurewindBearingcloudCoverozoneprecipIntensityMaxmonthsourcedestinationcab_typenameshort_summaryuvIndex
hour1.0000.0340.0010.002-0.0050.233-0.088-0.076-0.2860.0950.1040.154-0.014-0.002-0.002-0.0830.0060.0320.0520.0090.1150.0000.0090.0110.0000.2000.699
day0.0341.0000.0050.000-0.001-0.094-0.028-0.033-0.1350.5040.5040.255-0.254-0.125-0.171-0.5230.209-0.0550.392-0.0661.0000.0080.0030.0260.0040.2620.119
price0.0010.0051.0000.3080.1660.0200.0110.0110.0120.0080.008-0.0070.0110.0160.020-0.006-0.0110.012-0.0070.0010.0060.0660.0710.1510.4100.0180.000
distance0.0020.0000.3081.0000.0220.0080.0060.006-0.0000.0110.014-0.0110.0020.0040.0070.002-0.0060.0060.0060.0110.0000.3150.3330.1040.0260.0000.000
surge_multiplier-0.005-0.0010.1660.0221.0000.000-0.012-0.013-0.005-0.003-0.0020.011-0.001-0.008-0.0050.0090.008-0.009-0.005-0.0020.0000.0590.0460.2040.1050.0130.000
temperature0.233-0.0940.0200.0080.0001.0000.3270.3270.3020.1160.108-0.2430.7570.4670.818-0.255-0.3550.386-0.2780.3910.4640.0000.0070.0020.0000.2460.220
precipIntensity-0.088-0.0280.0110.006-0.0120.3271.0000.9980.5870.2370.154-0.6210.2360.2430.536-0.157-0.3810.541-0.2280.5750.2060.0000.0000.0000.0040.5000.112
precipProbability-0.076-0.0330.0110.006-0.0130.3270.9981.0000.5890.2330.150-0.6230.2370.2410.539-0.160-0.3790.543-0.2280.5760.1450.0000.0110.0110.0030.6220.168
humidity-0.286-0.1350.012-0.000-0.0050.3020.5870.5891.000-0.172-0.228-0.6640.4530.4350.724-0.133-0.3810.541-0.4080.5180.1940.0000.0000.0000.0000.3180.256
windSpeed0.0950.5040.0080.011-0.0030.1160.2370.233-0.1721.0000.9550.159-0.120-0.1920.054-0.5930.1670.1540.5160.1730.5070.0000.0000.0000.0050.2760.207
windGust0.1040.5040.0080.014-0.0020.1080.1540.150-0.2280.9551.0000.214-0.115-0.2000.018-0.5830.1690.1000.5560.1290.5080.0000.0110.0000.0000.2580.263
visibility0.1540.255-0.007-0.0110.011-0.243-0.621-0.623-0.6640.1590.2141.000-0.285-0.225-0.478-0.0290.299-0.4670.358-0.4080.1780.0000.0000.0000.0000.4560.130
temperatureHigh-0.014-0.2540.0110.002-0.0010.7570.2360.2370.453-0.120-0.115-0.2851.0000.5830.715-0.063-0.3790.146-0.5190.3030.6420.0080.0000.0200.0000.2560.096
temperatureLow-0.002-0.1250.0160.004-0.0080.4670.2430.2410.435-0.192-0.200-0.2250.5831.0000.5140.041-0.4300.269-0.3960.0670.7060.0050.0000.0230.0090.3250.130
dewPoint-0.002-0.1710.0200.007-0.0050.8180.5360.5390.7240.0540.018-0.4780.7150.5141.000-0.284-0.4700.584-0.2980.6070.5060.0000.0070.0000.0090.3230.183
pressure-0.083-0.523-0.0060.0020.009-0.255-0.157-0.160-0.133-0.593-0.583-0.029-0.0630.041-0.2841.000-0.200-0.276-0.457-0.1770.7460.0070.0090.0090.0020.2540.219
windBearing0.0060.209-0.011-0.0060.008-0.355-0.381-0.379-0.3810.1670.1690.299-0.379-0.430-0.470-0.2001.000-0.2950.303-0.4760.4840.0000.0000.0000.0000.3310.223
cloudCover0.032-0.0550.0120.006-0.0090.3860.5410.5430.5410.1540.100-0.4670.1460.2690.584-0.276-0.2951.000-0.0330.4330.1440.0000.0050.0040.0100.5920.185
ozone0.0520.392-0.0070.006-0.005-0.278-0.228-0.228-0.4080.5160.5560.358-0.519-0.396-0.298-0.4570.303-0.0331.000-0.1360.5610.0080.0110.0210.0000.2250.277
precipIntensityMax0.009-0.0660.0010.011-0.0020.3910.5750.5760.5180.1730.129-0.4080.3030.0670.607-0.177-0.4760.433-0.1361.0000.4460.0030.0000.0180.0070.3880.081
month0.1151.0000.0060.0000.0000.4640.2060.1450.1940.5070.5080.1780.6420.7060.5060.7460.4840.1440.5610.4461.0000.0210.0160.0190.0240.1790.047
source0.0000.0080.0660.3150.0590.0000.0000.0000.0000.0000.0000.0000.0080.0050.0000.0070.0000.0000.0080.0030.0211.0000.3050.0690.0000.0050.000
destination0.0090.0030.0710.3330.0460.0070.0000.0110.0000.0000.0110.0000.0000.0000.0070.0090.0000.0050.0110.0000.0160.3051.0000.0780.0060.0110.007
cab_type0.0110.0260.1510.1040.2040.0020.0000.0110.0000.0000.0000.0000.0200.0230.0000.0090.0000.0040.0210.0180.0190.0690.0781.0001.0000.0060.013
name0.0000.0040.4100.0260.1050.0000.0040.0030.0000.0050.0000.0000.0000.0090.0090.0020.0000.0100.0000.0070.0240.0000.0061.0001.0000.0060.000
short_summary0.2000.2620.0180.0000.0130.2460.5000.6220.3180.2760.2580.4560.2560.3250.3230.2540.3310.5920.2250.3880.1790.0050.0110.0060.0061.0000.171
uvIndex0.6990.1190.0000.0000.0000.2200.1120.1680.2560.2070.2630.1300.0960.1300.1830.2190.2230.1850.2770.0810.0470.0000.0070.0130.0000.1711.000

Missing values

2023-03-19T20:14:00.402555image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-19T20:14:00.968015image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

hourdaymonthsourcedestinationcab_typenamepricedistancesurge_multipliertemperatureshort_summaryprecipIntensityprecipProbabilityhumiditywindSpeedwindGustvisibilitytemperatureHightemperatureLowdewPointpressurewindBearingcloudCoveruvIndexozoneprecipIntensityMax
091612Haymarket SquareNorth StationLyftShared5.00.441.042.34Mostly Cloudy0.00000.00.688.669.1710.00043.6834.1932.701021.98570.720303.80.1276
122711Haymarket SquareNorth StationLyftLux11.00.441.043.58Rain0.12991.00.9411.9811.984.78647.3042.1041.831003.97901.000291.10.1300
212811Haymarket SquareNorth StationLyftLyft7.00.441.038.33Clear0.00000.00.757.337.3310.00047.5533.1031.10992.282400.030315.70.1064
343011Haymarket SquareNorth StationLyftLux Black XL26.00.441.034.38Clear0.00000.00.735.285.2810.00045.0328.9026.641013.733100.000291.10.0000
432911Haymarket SquareNorth StationLyftLyft XL9.00.441.037.44Partly Cloudy0.00000.00.709.149.1410.00042.1836.7128.61998.363030.440347.70.0001
5181712Haymarket SquareNorth StationLyftLux Black16.50.441.038.75Overcast0.00000.00.847.198.888.32540.6124.0734.411000.462941.001335.80.0221
652611Back BayNortheastern UniversityLyftLyft XL10.51.081.041.99Overcast0.00000.00.910.530.884.67546.4642.1739.541014.11911.000312.30.1245
719212Back BayNortheastern UniversityLyftLux Black16.51.081.049.88Light Rain0.02461.00.933.383.383.05250.8044.9748.021004.331591.000282.50.0916
86312Back BayNortheastern UniversityLyftShared3.01.081.045.58Foggy0.00000.00.961.252.091.41357.0233.7444.501001.063071.000290.90.0004
9102711Back BayNortheastern UniversityLyftLux Black XL27.51.081.045.45Light Rain0.06241.00.936.877.422.68646.9133.8243.52989.98791.000296.20.1425
hourdaymonthsourcedestinationcab_typenamepricedistancesurge_multipliertemperatureshort_summaryprecipIntensityprecipProbabilityhumiditywindSpeedwindGustvisibilitytemperatureHightemperatureLowdewPointpressurewindBearingcloudCoveruvIndexozoneprecipIntensityMax
19990192711Haymarket SquareNorth StationLyftLux Black XL26.00.581.042.95Mostly Cloudy0.00000.000.729.4213.8010.00046.8333.7534.56990.252570.810313.60.1433
199910412Haymarket SquareNorth StationLyftLyft XL9.00.581.046.36Clear0.00000.000.574.036.3210.00057.4233.6031.821002.032660.010326.90.0004
19992163011North EndTheatre DistrictUberBlack SUV26.01.411.040.13Clear0.00000.000.623.464.479.92042.3231.5727.991016.842910.122269.90.0004
199936212North EndTheatre DistrictUberUberPool8.51.411.037.96Overcast0.00000.000.841.903.299.70650.3744.7933.591021.231351.000269.60.0956
1999432711North EndTheatre DistrictUberUberX8.51.411.043.73Rain0.10881.000.8911.1416.894.50346.4941.9040.831001.71791.000290.30.1225
19995191312FenwayTheatre DistrictLyftLux19.52.691.032.85Mostly Cloudy0.00000.000.562.653.839.95933.8327.2718.661033.65760.640330.80.0001
19996191612FenwayTheatre DistrictLyftLyft11.02.691.043.06Overcast0.00000.000.718.1213.4410.00043.7434.0734.251015.00711.000322.70.1246
1999761812FenwayTheatre DistrictLyftLyft XL16.52.691.033.71Overcast0.00170.110.6511.0520.205.86032.7520.6623.091001.283141.000362.10.0028
19998102611FenwayTheatre DistrictLyftLux Black XL34.02.691.041.40Foggy0.00000.000.921.811.810.96546.3942.1539.381015.4121.000298.30.1361
19999101412FenwayTheatre DistrictLyftLux Black26.02.691.027.71Partly Cloudy0.00000.000.792.964.759.64146.6740.7322.071033.841480.140294.70.0000

Duplicate rows

Most frequently occurring

hourdaymonthsourcedestinationcab_typenamepricedistancesurge_multipliertemperatureshort_summaryprecipIntensityprecipProbabilityhumiditywindSpeedwindGustvisibilitytemperatureHightemperatureLowdewPointpressurewindBearingcloudCoveruvIndexozoneprecipIntensityMax# duplicates
1512911Boston UniversityBack BayUberTaxi16.4305251.561.038.42Mostly Cloudy0.00000.00.728.4111.549.91542.6137.6030.30996.922970.770349.90.00003
2222911West EndHaymarket SquareUberTaxi16.4305250.721.037.92Mostly Cloudy0.00000.00.718.2812.439.98142.7037.1729.42997.373050.750348.90.00003
002711Beacon HillHaymarket SquareUberBlack SUV27.5000001.351.044.52Rain0.10581.00.918.338.403.79046.4941.9042.171006.47891.000296.40.12252
102711Haymarket SquareTheatre DistrictUberTaxi16.4305251.161.044.52Rain0.10581.00.918.338.403.79046.4941.9042.171006.47891.000296.40.12252
202711West EndBoston UniversityLyftLux19.5000002.881.044.52Rain0.10581.00.918.338.403.79046.4941.9042.171006.47891.000296.40.12252
302811FenwayBeacon HillUberUberX9.5000002.461.039.13Clear0.00000.00.737.0310.5110.00046.8333.7531.05991.992460.120315.10.14332
402811Haymarket SquareNorth StationUberUberXL10.5000000.491.039.13Clear0.00000.00.737.0310.5110.00046.8333.7531.05991.992460.120315.10.14332
502911Back BayFenwayUberTaxi16.4305251.341.039.41Mostly Cloudy0.00000.00.698.1112.219.99642.7237.5930.16996.212940.810352.80.00002
602911FenwayWest EndUberUberXL15.0000002.721.039.41Mostly Cloudy0.00000.00.698.1112.219.99642.7237.5930.16996.212940.810352.80.00002
702911North StationHaymarket SquareUberUberX7.0000000.561.039.41Mostly Cloudy0.00000.00.698.1112.219.99642.7237.5930.16996.212940.810352.80.00002